Fine-Tuning Large Language Models Using QLoRA Training Course

QLoRA is an advanced method for fine-tuning large language models (LLMs) by using quantization techniques. This approach offers a more efficient way to fine-tune these models without incurring significant computational costs. The training will delve into both the theoretical foundations and practical implementation of fine-tuning LLMs with QLoRA.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced machine learning engineers, AI developers, and data scientists who are interested in learning how to use QLoRA to efficiently fine-tune large models for specific tasks and customizations.

By the end of this training, participants will be able to:

Grasp the theory behind QLoRA and quantization techniques for LLMs.
Implement QLoRA in the fine-tuning process of large language models for domain-specific applications.
Optimize fine-tuning performance on limited computational resources using quantization methods.
Deploy and evaluate fine-tuned models efficiently in real-world scenarios.

Format of the Course

Interactive lectures and discussions.
Ample exercises and practice sessions.
Hands-on implementation in a live-lab environment.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

This course is available as onsite live training in Uzbekistan or online live training.

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Course Outline

Introduction to QLoRA and Quantization

Overview of quantization and its role in model optimization
Introduction to QLoRA framework and its benefits
Key differences between QLoRA and traditional fine-tuning methods

Fundamentals of Large Language Models (LLMs)

Introduction to LLMs and their architecture
Challenges of fine-tuning large models at scale
How quantization helps overcome computational constraints in LLM fine-tuning

Implementing QLoRA for Fine-Tuning LLMs

Setting up the QLoRA framework and environment
Preparing datasets for QLoRA fine-tuning
Step-by-step guide to implementing QLoRA on LLMs using Python and PyTorch/TensorFlow

Optimizing Fine-Tuning Performance with QLoRA

How to balance model accuracy and performance with quantization
Techniques for reducing compute costs and memory usage during fine-tuning
Strategies for fine-tuning with minimal hardware requirements

Evaluating Fine-Tuned Models

How to assess the effectiveness of fine-tuned models
Common evaluation metrics for language models
Optimizing model performance post-tuning and troubleshooting issues

Deploying and Scaling Fine-Tuned Models

Best practices for deploying quantized LLMs into production environments
Scaling deployment to handle real-time requests
Tools and frameworks for model deployment and monitoring

Real-World Use Cases and Case Studies

Case study: Fine-tuning LLMs for customer support and NLP tasks
Examples of fine-tuning LLMs in various industries like healthcare, finance, and e-commerce
Lessons learned from real-world deployments of QLoRA-based models

Summary and Next Steps

Requirements

An understanding of machine learning fundamentals and neural networks
Experience with model fine-tuning and transfer learning
Familiarity with large language models (LLMs) and deep learning frameworks (e.g., PyTorch, TensorFlow)

Audience

Machine learning engineers
AI developers
Data scientists

14 Hours

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Fine-Tuning Large Language Models Using QLoRA Training Course

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Fine-Tuning Large Language Models Using QLoRA Training Course

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